Delivering online cognitive behavioural therapy to address mental health challenges in correctional workers: A randomized controlled trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Correctional workers (CWs), are frequently exposed to traumatic workplace events, placing them at higher risk of experiencing mental health disorders, including major depressive disorder (MDD), generalized anxiety disorder (GAD), and posttraumatic stress disorder (PTSD). Despite high prevalence of mental health challenges, CWs underutilize mental health services due to stigma, irregular work schedules, and accessibility issues. While cognitive behavioural therapy (CBT) is effective, it poses accessibility challenges. Electronic CBT (eCBT) offers a scalable, accessible alternative, but use among CWs remains underexplored. OBJECTIVE This study aimed to evaluate the efficacy of diagnosis-specific eCBT programs designed for CWs in reducing symptoms of MDD, GAD, and PTSD compared to treatment as usual (TAU). METHODS A randomized controlled trial (RCT) was conducted with 84 CWs in Ontario, assigned to either eCBT (n=40) or TAU (n=44). The eCBT program provided diagnosis-specific modules with synchronous personalized feedback from care providers over 12-weeks via the Online Psychotherapy Tool (OPTT). Symptom severity was measured at baseline, week-6, and post-treatment using validated scales for depression, anxiety, PTSD, and quality-of-life. Data analysis included paired samples t-tests, mixed linear models, and effect size calculations (Cohen's d). RESULTS The eCBT group exhibited significant reductions in symptom severity for depression, anxiety, and PTSD from baseline to post-treatment compared to TAU (p<0.01), with a moderate effect size (Cohen’s d=0.68). Symptom severity decreased by 61.37% for eCBT versus 23.29% in TAU. Mixed linear models confirmed a significant treatment-by-time interaction, favouring eCBT (p<0.01). However, no significant differences in quality-of-life improvements were observed between groups. CONCLUSIONS Diagnosis-specific eCBT programs are effective in reducing symptoms of depression, anxiety, and PTSD for CWs. The asynchronous, accessible format of eCBT addresses current key barriers. Future research should explore strategies to improve adherence and increase the accessibility of services for CWs. CLINICALTRIAL Clinicaltrials.gov (NCT04666974). INTERNATIONAL REGISTERED REPORT RR2-10.2196/30845
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".